1. Field of the Invention
The present invention is related to providing weather forecasts on wide geographic areas, and more particularly, to adjusting weather forecasts in response to population movement in a wide geographic area.
2. Background Description
Weather forecasts are based on weather data collected from sensors that are located over a large geographic area or even worldwide. In forecasting the weather for a wide geographic area, the area typically is divided into smaller more manageable units by a superimposing a grid over the area. Then, the relationship of the weather data among the several units or grid locations is described in several algebraic equations, e.g., using a Finite Element Model (FEM) for the gridded area. Frequently, the FEM requires a considerable, even excessive, amount of data processing resources.
Moreover, the higher the grid resolution, the larger the number of units, the more complex the FEM equations and, correspondingly, the more data processing resources consumed in generating weather forecasts. The data processing demands may be such that, it may be infeasible to provide real time or even timely forecasts for all grid locations. This is especially troublesome when, as is commonly the case, forecast results are subject to tight delivery deadlines. What is commonly known as adaptive mesh refinement (AMR) is a type of dynamic mesh refinement that has been used to selectively provide real time forecasts.
Adaptive mesh refinement begins with a low resolution grid for an area. The weather map contains coarse-grained cells to provide rough initial forecasts. Where more detailed forecasts are necessary for certain cells, provided there is sufficient data and time available, those cells are further refined. Typically, refinement is based on quality and quantity of sensors in the area, i.e., focus is on areas with more and better sensors. B. Plale et al., “CASA and LEAD: Adaptive Cyberinfrastructure for Real-Time Multiscale Weather Forecasting,” IEEE Computer Magazine, 2006, provides an example of sensor based refinement, that focuses grid refinement on the sensors, i.e. sensor quality and quantity. However, sensor based refinement may not refine the forecast grid where people are, much less where they are headed.
Even when population is considered in forecasting, e.g., by sensor placement or otherwise, heavy weather, e.g., tornadoes or hurricanes, or local emergencies, may result in conditions that cause local evacuations. State of the art forecasting does not consider these emergencies in gridding an area. How the local populace evacuates an area, an example of swarm behavior, can vary widely depending on the situation, population and locale. Since almost by definition, evacuation means emptying a relatively densely populated area into relatively empty or less dense, low population areas, evacuating part of an area tends to render any current weather forecasts stale and inadequate.
Thus, there is a need for efficiently providing real time weather forecasts for large areas with a fluid population distribution; and, more particularly for efficiently and quickly adjusting weather forecasts for local population swarm behavior.